Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations7051560
Missing cells0
Missing cells (%)0.0%
Duplicate rows140696
Duplicate rows (%)2.0%
Total size in memory813.7 MiB
Average record size in memory121.0 B

Variable types

Categorical3
Numeric7
Text3
Boolean9

Alerts

Dataset has 140696 (2.0%) duplicate rowsDuplicates
Severity is highly imbalanced (56.3%) Imbalance
Amenity is highly imbalanced (90.3%) Imbalance
Give_Way is highly imbalanced (95.7%) Imbalance
Junction is highly imbalanced (62.3%) Imbalance
No_Exit is highly imbalanced (97.4%) Imbalance
Railway is highly imbalanced (92.9%) Imbalance
Stop is highly imbalanced (81.7%) Imbalance
Traffic_Calming is highly imbalanced (98.9%) Imbalance
Duration_Seconds is highly skewed (γ1 = 50.61168934) Skewed
Distance(mi) has 2959002 (42.0%) zeros Zeros
Wind_Speed(mph) has 942813 (13.4%) zeros Zeros

Reproduction

Analysis started2024-11-04 23:33:25.030150
Analysis finished2024-11-04 23:36:35.560166
Duration3 minutes and 10.53 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Severity
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 MiB
2
5671504 
3
1136467 
4
 
178538
1
 
65051

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7051560
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 5671504
80.4%
3 1136467
 
16.1%
4 178538
 
2.5%
1 65051
 
0.9%

Length

2024-11-05T00:36:35.611135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T00:36:35.672142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5671504
80.4%
3 1136467
 
16.1%
4 178538
 
2.5%
1 65051
 
0.9%

Most occurring characters

ValueCountFrequency (%)
2 5671504
80.4%
3 1136467
 
16.1%
4 178538
 
2.5%
1 65051
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7051560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 5671504
80.4%
3 1136467
 
16.1%
4 178538
 
2.5%
1 65051
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7051560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 5671504
80.4%
3 1136467
 
16.1%
4 178538
 
2.5%
1 65051
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7051560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 5671504
80.4%
3 1136467
 
16.1%
4 178538
 
2.5%
1 65051
 
0.9%

Distance(mi)
Real number (ℝ)

Zeros 

Distinct21687
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56854528
Minimum0
Maximum441.75
Zeros2959002
Zeros (%)42.0%
Negative0
Negative (%)0.0%
Memory size107.6 MiB
2024-11-05T00:36:35.739567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.037
Q30.478
95-th percentile2.697
Maximum441.75
Range441.75
Interquartile range (IQR)0.478

Descriptive statistics

Standard deviation1.7641632
Coefficient of variation (CV)3.1029423
Kurtosis1583.1938
Mean0.56854528
Median Absolute Deviation (MAD)0.037
Skewness19.843757
Sum4009131.2
Variance3.1122718
MonotonicityNot monotonic
2024-11-05T00:36:35.816617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2959002
42.0%
0.01 212576
 
3.0%
0.008 13880
 
0.2%
0.009 13182
 
0.2%
0.007 11733
 
0.2%
0.009999999776 11083
 
0.2%
0.011 11011
 
0.2%
0.03 10801
 
0.2%
0.024 10372
 
0.1%
0.028 10332
 
0.1%
Other values (21677) 3787588
53.7%
ValueCountFrequency (%)
0 2959002
42.0%
0.001 4680
 
0.1%
0.002 2694
 
< 0.1%
0.003 3934
 
0.1%
0.004 5931
 
0.1%
0.005 7752
 
0.1%
0.006 9600
 
0.1%
0.007 11733
 
0.2%
0.008 13880
 
0.2%
0.009 13182
 
0.2%
ValueCountFrequency (%)
441.75 1
< 0.1%
336.5700073 1
< 0.1%
254.3999939 1
< 0.1%
251.2200012 1
< 0.1%
242.3399963 1
< 0.1%
224.5899963 1
< 0.1%
210.0800018 1
< 0.1%
194.7299957 1
< 0.1%
193.4799957 1
< 0.1%
183.1199951 1
< 0.1%

Street
Text

Distinct320207
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size107.6 MiB
2024-11-05T00:36:36.055664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length59
Median length47
Mean length11.063362
Min length1

Characters and Unicode

Total characters78013961
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124428 ?
Unique (%)1.8%

Sample

1st rowState Route 32
2nd rowI-75 S
3rd rowMiamisburg Centerville Rd
4th rowWesterville Rd
5th rowN Woodward Ave
ValueCountFrequency (%)
n 1092408
 
6.2%
s 1088072
 
6.2%
rd 1056801
 
6.0%
w 859562
 
4.9%
e 849902
 
4.9%
st 629910
 
3.6%
ave 590604
 
3.4%
blvd 314610
 
1.8%
dr 301258
 
1.7%
fwy 286513
 
1.6%
Other values (71822) 10436013
59.6%
2024-11-05T00:36:36.349698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12059741
 
15.5%
e 4346299
 
5.6%
a 3471039
 
4.4%
r 2955808
 
3.8%
t 2936495
 
3.8%
S 2722829
 
3.5%
o 2720548
 
3.5%
n 2713466
 
3.5%
d 2540155
 
3.3%
l 2520382
 
3.2%
Other values (70) 39027199
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78013961
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12059741
 
15.5%
e 4346299
 
5.6%
a 3471039
 
4.4%
r 2955808
 
3.8%
t 2936495
 
3.8%
S 2722829
 
3.5%
o 2720548
 
3.5%
n 2713466
 
3.5%
d 2540155
 
3.3%
l 2520382
 
3.2%
Other values (70) 39027199
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78013961
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12059741
 
15.5%
e 4346299
 
5.6%
a 3471039
 
4.4%
r 2955808
 
3.8%
t 2936495
 
3.8%
S 2722829
 
3.5%
o 2720548
 
3.5%
n 2713466
 
3.5%
d 2540155
 
3.3%
l 2520382
 
3.2%
Other values (70) 39027199
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78013961
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12059741
 
15.5%
e 4346299
 
5.6%
a 3471039
 
4.4%
r 2955808
 
3.8%
t 2936495
 
3.8%
S 2722829
 
3.5%
o 2720548
 
3.5%
n 2713466
 
3.5%
d 2540155
 
3.3%
l 2520382
 
3.2%
Other values (70) 39027199
50.0%
Distinct774620
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size107.6 MiB
2024-11-05T00:36:36.856384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length6.4828095
Min length5

Characters and Unicode

Total characters45713920
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique426008 ?
Unique (%)6.0%

Sample

1st row45176
2nd row45417
3rd row45459
4th row43081
5th row45417-2476
ValueCountFrequency (%)
91761 10683
 
0.2%
91706 8692
 
0.1%
92507 8177
 
0.1%
92407 8071
 
0.1%
33186 7929
 
0.1%
32819 7269
 
0.1%
33169 6658
 
0.1%
75243 6570
 
0.1%
92324 6322
 
0.1%
90805 6244
 
0.1%
Other values (774610) 6974945
98.9%
2024-11-05T00:36:37.382874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5737493
12.6%
2 5601412
12.3%
3 5244330
11.5%
1 5204712
11.4%
9 4041057
8.8%
7 3954685
8.7%
5 3858349
8.4%
4 3801049
8.3%
6 3151443
6.9%
8 3022532
6.6%
Other values (3) 2096858
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45713920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5737493
12.6%
2 5601412
12.3%
3 5244330
11.5%
1 5204712
11.4%
9 4041057
8.8%
7 3954685
8.7%
5 3858349
8.4%
4 3801049
8.3%
6 3151443
6.9%
8 3022532
6.6%
Other values (3) 2096858
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45713920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5737493
12.6%
2 5601412
12.3%
3 5244330
11.5%
1 5204712
11.4%
9 4041057
8.8%
7 3954685
8.7%
5 3858349
8.4%
4 3801049
8.3%
6 3151443
6.9%
8 3022532
6.6%
Other values (3) 2096858
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45713920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5737493
12.6%
2 5601412
12.3%
3 5244330
11.5%
1 5204712
11.4%
9 4041057
8.8%
7 3954685
8.7%
5 3858349
8.4%
4 3801049
8.3%
6 3151443
6.9%
8 3022532
6.6%
Other values (3) 2096858
 
4.6%

Temperature(F)
Real number (ℝ)

Distinct829
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.869611
Minimum-45
Maximum196
Zeros2651
Zeros (%)< 0.1%
Negative18053
Negative (%)0.3%
Memory size107.6 MiB
2024-11-05T00:36:37.462386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-45
5-th percentile28
Q149
median64
Q376
95-th percentile89
Maximum196
Range241
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.042054
Coefficient of variation (CV)0.30777717
Kurtosis-0.014583106
Mean61.869611
Median Absolute Deviation (MAD)13
Skewness-0.52138711
Sum4.3627728 × 108
Variance362.59983
MonotonicityNot monotonic
2024-11-05T00:36:37.539570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 162688
 
2.3%
77 160720
 
2.3%
72 152716
 
2.2%
68 151851
 
2.2%
75 150957
 
2.1%
70 147290
 
2.1%
63 141625
 
2.0%
79 141137
 
2.0%
64 139891
 
2.0%
66 136900
 
1.9%
Other values (819) 5565785
78.9%
ValueCountFrequency (%)
-45 1
 
< 0.1%
-38 3
 
< 0.1%
-36 2
 
< 0.1%
-35 9
< 0.1%
-33 1
 
< 0.1%
-30 1
 
< 0.1%
-29 8
< 0.1%
-28 5
< 0.1%
-27.9 12
< 0.1%
-27.4 3
 
< 0.1%
ValueCountFrequency (%)
196 5
< 0.1%
189 1
 
< 0.1%
174 2
 
< 0.1%
172 2
 
< 0.1%
170.6 1
 
< 0.1%
168.8 1
 
< 0.1%
167 1
 
< 0.1%
162 2
 
< 0.1%
161.6 1
 
< 0.1%
143.6 1
 
< 0.1%

Humidity(%)
Real number (ℝ)

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.415366
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.6 MiB
2024-11-05T00:36:37.612242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24
Q148
median67
Q384
95-th percentile96
Maximum100
Range99
Interquartile range (IQR)36

Descriptive statistics

Standard deviation22.757178
Coefficient of variation (CV)0.35328803
Kurtosis-0.72760491
Mean64.415366
Median Absolute Deviation (MAD)18
Skewness-0.38008968
Sum4.5422882 × 108
Variance517.88913
MonotonicityNot monotonic
2024-11-05T00:36:37.686667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 264622
 
3.8%
100 246092
 
3.5%
87 156275
 
2.2%
90 154061
 
2.2%
89 128993
 
1.8%
96 120746
 
1.7%
81 117405
 
1.7%
84 117210
 
1.7%
82 114896
 
1.6%
86 112259
 
1.6%
Other values (90) 5519001
78.3%
ValueCountFrequency (%)
1 45
 
< 0.1%
2 189
 
< 0.1%
3 656
 
< 0.1%
4 2070
 
< 0.1%
5 3970
 
0.1%
6 5781
0.1%
7 7731
0.1%
8 9169
0.1%
9 10550
0.1%
10 12848
0.2%
ValueCountFrequency (%)
100 246092
3.5%
99 12864
 
0.2%
98 6129
 
0.1%
97 79382
 
1.1%
96 120746
1.7%
95 8565
 
0.1%
94 104894
 
1.5%
93 264622
3.8%
92 60688
 
0.9%
91 34646
 
0.5%

Pressure(in)
Real number (ℝ)

Distinct1126
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.514437
Minimum0
Maximum58.63
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size107.6 MiB
2024-11-05T00:36:37.755823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.85
Q129.34
median29.84
Q330.02
95-th percentile30.24
Maximum58.63
Range58.63
Interquartile range (IQR)0.68

Descriptive statistics

Standard deviation1.0148514
Coefficient of variation (CV)0.034384914
Kurtosis19.955612
Mean29.514437
Median Absolute Deviation (MAD)0.25
Skewness-3.5478432
Sum2.0812282 × 108
Variance1.0299233
MonotonicityNot monotonic
2024-11-05T00:36:37.826001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.96 111934
 
1.6%
29.99 109652
 
1.6%
29.94 108617
 
1.5%
30.01 106437
 
1.5%
29.97 102676
 
1.5%
29.91 101754
 
1.4%
29.95 100739
 
1.4%
30.04 100576
 
1.4%
30.03 99319
 
1.4%
30 99191
 
1.4%
Other values (1116) 6010665
85.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.02 1
 
< 0.1%
0.29 2
 
< 0.1%
0.39 1
 
< 0.1%
2.99 5
< 0.1%
3 2
 
< 0.1%
3.01 1
 
< 0.1%
3.04 4
< 0.1%
9.9 2
 
< 0.1%
16.71 1
 
< 0.1%
ValueCountFrequency (%)
58.63 7
< 0.1%
58.39 2
 
< 0.1%
58.32 1
 
< 0.1%
58.13 1
 
< 0.1%
58.1 4
< 0.1%
58.04 3
< 0.1%
57.74 1
 
< 0.1%
57.54 2
 
< 0.1%
56.54 2
 
< 0.1%
56.31 1
 
< 0.1%

Visibility(mi)
Real number (ℝ)

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1070711
Minimum0
Maximum140
Zeros7236
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size107.6 MiB
2024-11-05T00:36:37.893315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5
Q110
median10
Q310
95-th percentile10
Maximum140
Range140
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6422642
Coefficient of variation (CV)0.29013326
Kurtosis80.462982
Mean9.1070711
Median Absolute Deviation (MAD)0
Skewness2.1147471
Sum64219058
Variance6.98156
MonotonicityNot monotonic
2024-11-05T00:36:37.966753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 5701259
80.9%
7 194895
 
2.8%
9 172317
 
2.4%
8 136720
 
1.9%
5 130384
 
1.8%
6 116029
 
1.6%
2 115544
 
1.6%
4 109570
 
1.6%
3 108489
 
1.5%
1 98051
 
1.4%
Other values (71) 168302
 
2.4%
ValueCountFrequency (%)
0 7236
 
0.1%
0.06 316
 
< 0.1%
0.1 870
 
< 0.1%
0.12 1749
 
< 0.1%
0.19 41
 
< 0.1%
0.2 7184
 
0.1%
0.25 26547
0.4%
0.31 4
 
< 0.1%
0.38 328
 
< 0.1%
0.4 52
 
< 0.1%
ValueCountFrequency (%)
140 1
 
< 0.1%
111 3
 
< 0.1%
105 1
 
< 0.1%
101 1
 
< 0.1%
100 47
 
< 0.1%
98 1
 
< 0.1%
90 13
 
< 0.1%
80 295
< 0.1%
78 1
 
< 0.1%
76 3
 
< 0.1%

Wind_Direction
Categorical

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 MiB
CALM
942803 
S
 
413902
SSW
 
379701
W
 
378400
WNW
 
373374
Other values (18)
4563380 

Length

Max length8
Median length5
Mean length2.7674176
Min length1

Characters and Unicode

Total characters19514611
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSW
2nd rowSW
3rd rowSW
4th rowSSW
5th rowWSW

Common Values

ValueCountFrequency (%)
CALM 942803
 
13.4%
S 413902
 
5.9%
SSW 379701
 
5.4%
W 378400
 
5.4%
WNW 373374
 
5.3%
NW 364394
 
5.2%
SW 359826
 
5.1%
WSW 348591
 
4.9%
SSE 344112
 
4.9%
NNW 328354
 
4.7%
Other values (13) 2818103
40.0%

Length

2024-11-05T00:36:38.042604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
calm 942803
 
13.4%
s 413902
 
5.9%
ssw 379701
 
5.4%
w 378400
 
5.4%
wnw 373374
 
5.3%
nw 364394
 
5.2%
sw 359826
 
5.1%
wsw 348591
 
4.9%
sse 344112
 
4.9%
nnw 328354
 
4.7%
Other values (13) 2818103
40.0%

Most occurring characters

ValueCountFrequency (%)
W 3416992
17.5%
S 3301407
16.9%
N 2833882
14.5%
E 2558297
13.1%
A 1189944
 
6.1%
M 942803
 
4.8%
L 942803
 
4.8%
C 942803
 
4.8%
t 564925
 
2.9%
V 359219
 
1.8%
Other values (11) 2461536
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19514611
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 3416992
17.5%
S 3301407
16.9%
N 2833882
14.5%
E 2558297
13.1%
A 1189944
 
6.1%
M 942803
 
4.8%
L 942803
 
4.8%
C 942803
 
4.8%
t 564925
 
2.9%
V 359219
 
1.8%
Other values (11) 2461536
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19514611
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 3416992
17.5%
S 3301407
16.9%
N 2833882
14.5%
E 2558297
13.1%
A 1189944
 
6.1%
M 942803
 
4.8%
L 942803
 
4.8%
C 942803
 
4.8%
t 564925
 
2.9%
V 359219
 
1.8%
Other values (11) 2461536
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19514611
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 3416992
17.5%
S 3301407
16.9%
N 2833882
14.5%
E 2558297
13.1%
A 1189944
 
6.1%
M 942803
 
4.8%
L 942803
 
4.8%
C 942803
 
4.8%
t 564925
 
2.9%
V 359219
 
1.8%
Other values (11) 2461536
12.6%

Wind_Speed(mph)
Real number (ℝ)

Zeros 

Distinct179
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6911295
Minimum0
Maximum1087
Zeros942813
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size107.6 MiB
2024-11-05T00:36:38.112184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6
median7
Q310.4
95-th percentile17
Maximum1087
Range1087
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation5.4122238
Coefficient of variation (CV)0.70369688
Kurtosis1088.9651
Mean7.6911295
Median Absolute Deviation (MAD)3
Skewness7.9491258
Sum54234461
Variance29.292167
MonotonicityNot monotonic
2024-11-05T00:36:38.184116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 942813
 
13.4%
5 526299
 
7.5%
6 508834
 
7.2%
3 506264
 
7.2%
7 473560
 
6.7%
8 426369
 
6.0%
9 383468
 
5.4%
10 319588
 
4.5%
12 276284
 
3.9%
4.6 214994
 
3.0%
Other values (169) 2473087
35.1%
ValueCountFrequency (%)
0 942813
13.4%
1 166
 
< 0.1%
1.2 440
 
< 0.1%
2 427
 
< 0.1%
2.3 890
 
< 0.1%
3 506264
7.2%
3.5 201212
 
2.9%
4.6 214994
 
3.0%
5 526299
7.5%
5.8 213280
 
3.0%
ValueCountFrequency (%)
1087 1
 
< 0.1%
984 1
 
< 0.1%
822.8 7
< 0.1%
812 1
 
< 0.1%
703.1 2
 
< 0.1%
580 2
 
< 0.1%
471.8 1
 
< 0.1%
328 1
 
< 0.1%
255 1
 
< 0.1%
254.3 2
 
< 0.1%
Distinct140
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 MiB
2024-11-05T00:36:38.281030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length7.6752019
Min length3

Characters and Unicode

Total characters54122147
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowOvercast
2nd rowMostly Cloudy
3rd rowMostly Cloudy
4th rowLight Rain
5th rowOvercast
ValueCountFrequency (%)
fair 2536998
25.9%
cloudy 2472945
25.2%
mostly 983438
 
10.0%
partly 672146
 
6.9%
clear 615352
 
6.3%
light 519905
 
5.3%
rain 484808
 
4.9%
overcast 321868
 
3.3%
scattered 176183
 
1.8%
clouds 176183
 
1.8%
Other values (48) 840646
 
8.6%
2024-11-05T00:36:38.444748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 4957471
 
9.2%
a 4945655
 
9.1%
r 4471321
 
8.3%
y 4315734
 
8.0%
o 3950113
 
7.3%
i 3805617
 
7.0%
C 3264496
 
6.0%
d 2999461
 
5.5%
t 2966355
 
5.5%
2748912
 
5.1%
Other values (36) 15697012
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54122147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 4957471
 
9.2%
a 4945655
 
9.1%
r 4471321
 
8.3%
y 4315734
 
8.0%
o 3950113
 
7.3%
i 3805617
 
7.0%
C 3264496
 
6.0%
d 2999461
 
5.5%
t 2966355
 
5.5%
2748912
 
5.1%
Other values (36) 15697012
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54122147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 4957471
 
9.2%
a 4945655
 
9.1%
r 4471321
 
8.3%
y 4315734
 
8.0%
o 3950113
 
7.3%
i 3805617
 
7.0%
C 3264496
 
6.0%
d 2999461
 
5.5%
t 2966355
 
5.5%
2748912
 
5.1%
Other values (36) 15697012
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54122147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 4957471
 
9.2%
a 4945655
 
9.1%
r 4471321
 
8.3%
y 4315734
 
8.0%
o 3950113
 
7.3%
i 3805617
 
7.0%
C 3264496
 
6.0%
d 2999461
 
5.5%
t 2966355
 
5.5%
2748912
 
5.1%
Other values (36) 15697012
29.0%

Amenity
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.5 MiB
False
6963701 
True
 
87859
ValueCountFrequency (%)
False 6963701
98.8%
True 87859
 
1.2%
2024-11-05T00:36:38.507053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Crossing
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.5 MiB
False
6243875 
True
807685 
ValueCountFrequency (%)
False 6243875
88.5%
True 807685
 
11.5%
2024-11-05T00:36:38.554689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Give_Way
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.5 MiB
False
7018855 
True
 
32705
ValueCountFrequency (%)
False 7018855
99.5%
True 32705
 
0.5%
2024-11-05T00:36:38.602106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Junction
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.5 MiB
False
6537106 
True
 
514454
ValueCountFrequency (%)
False 6537106
92.7%
True 514454
 
7.3%
2024-11-05T00:36:38.652334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

No_Exit
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.5 MiB
False
7033447 
True
 
18113
ValueCountFrequency (%)
False 7033447
99.7%
True 18113
 
0.3%
2024-11-05T00:36:38.698918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Railway
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.5 MiB
False
6990994 
True
 
60566
ValueCountFrequency (%)
False 6990994
99.1%
True 60566
 
0.9%
2024-11-05T00:36:38.746992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Stop
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.5 MiB
False
6856170 
True
 
195390
ValueCountFrequency (%)
False 6856170
97.2%
True 195390
 
2.8%
2024-11-05T00:36:38.789813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Traffic_Calming
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.5 MiB
False
7044580 
True
 
6980
ValueCountFrequency (%)
False 7044580
99.9%
True 6980
 
0.1%
2024-11-05T00:36:38.837889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.5 MiB
False
6009292 
True
1042268 
ValueCountFrequency (%)
False 6009292
85.2%
True 1042268
 
14.8%
2024-11-05T00:36:38.893141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Civil_Twilight
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size107.6 MiB
Day
5246527 
Night
1805033 

Length

Max length5
Median length3
Mean length3.5119528
Min length3

Characters and Unicode

Total characters24764746
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowDay
3rd rowDay
4th rowDay
5th rowDay

Common Values

ValueCountFrequency (%)
Day 5246527
74.4%
Night 1805033
 
25.6%

Length

2024-11-05T00:36:38.955676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T00:36:39.013831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
day 5246527
74.4%
night 1805033
 
25.6%

Most occurring characters

ValueCountFrequency (%)
D 5246527
21.2%
a 5246527
21.2%
y 5246527
21.2%
N 1805033
 
7.3%
i 1805033
 
7.3%
g 1805033
 
7.3%
h 1805033
 
7.3%
t 1805033
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24764746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 5246527
21.2%
a 5246527
21.2%
y 5246527
21.2%
N 1805033
 
7.3%
i 1805033
 
7.3%
g 1805033
 
7.3%
h 1805033
 
7.3%
t 1805033
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24764746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 5246527
21.2%
a 5246527
21.2%
y 5246527
21.2%
N 1805033
 
7.3%
i 1805033
 
7.3%
g 1805033
 
7.3%
h 1805033
 
7.3%
t 1805033
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24764746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 5246527
21.2%
a 5246527
21.2%
y 5246527
21.2%
N 1805033
 
7.3%
i 1805033
 
7.3%
g 1805033
 
7.3%
h 1805033
 
7.3%
t 1805033
 
7.3%

Duration_Seconds
Real number (ℝ)

Skewed 

Distinct72892
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26947.592
Minimum120
Maximum1.6877634 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size107.6 MiB
2024-11-05T00:36:39.071697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile1718
Q12068
median4502
Q37539
95-th percentile21600
Maximum1.6877634 × 108
Range1.6877622 × 108
Interquartile range (IQR)5471

Descriptive statistics

Standard deviation816632.16
Coefficient of variation (CV)30.304458
Kurtosis3685.2728
Mean26947.592
Median Absolute Deviation (MAD)2707
Skewness50.611689
Sum1.9002256 × 1011
Variance6.6688808 × 1011
MonotonicityNot monotonic
2024-11-05T00:36:39.151605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21600 301728
 
4.3%
1800 101353
 
1.4%
2700 61354
 
0.9%
4500 58442
 
0.8%
3600 55270
 
0.8%
14400 50458
 
0.7%
1786 46694
 
0.7%
1785 46645
 
0.7%
1787 45598
 
0.6%
1784 45229
 
0.6%
Other values (72882) 6238789
88.5%
ValueCountFrequency (%)
120 2
 
< 0.1%
150 3
 
< 0.1%
152 1
 
< 0.1%
180 16
< 0.1%
210 6
 
< 0.1%
221 1
 
< 0.1%
229 1
 
< 0.1%
240 12
< 0.1%
270 5
 
< 0.1%
271 1
 
< 0.1%
ValueCountFrequency (%)
168776340 2
< 0.1%
134184345 1
 
< 0.1%
134181332 3
< 0.1%
134179838 3
< 0.1%
134176830 2
< 0.1%
106135755 1
 
< 0.1%
100954757 1
 
< 0.1%
94755540 1
 
< 0.1%
94697995 1
 
< 0.1%
94697990 1
 
< 0.1%

Interactions

2024-11-05T00:36:10.252055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:39.151195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:44.256682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:49.540426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:54.842850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:59.960456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:05.138725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:11.013764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:39.855681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:44.989579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:50.301424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:55.564659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:00.691380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:05.853194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:11.780940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:40.568067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:45.737514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:51.025926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:56.297664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:01.417841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:06.556689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:12.556563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:41.275293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:46.480896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:51.789469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:56.994951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:02.157218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:07.281375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:13.328189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:42.001942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:47.237775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:52.554754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:57.715209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:02.872775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:07.999469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:14.084022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:42.716897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:47.983770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:53.312155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:58.442398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:03.630307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:08.702443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:14.798620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:43.510798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:48.783653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:54.115792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:35:59.217603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:04.415979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:36:09.474699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-05T00:36:39.216267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AmenityCivil_TwilightCrossingDistance(mi)Duration_SecondsGive_WayHumidity(%)JunctionNo_ExitPressure(in)RailwaySeverityStopTemperature(F)Traffic_CalmingTraffic_SignalVisibility(mi)Wind_DirectionWind_Speed(mph)
Amenity1.0000.0060.1480.0000.0020.0060.0150.0260.0140.0230.0500.0380.0340.0100.0230.1050.0020.0230.000
Civil_Twilight0.0061.0000.0390.0030.0070.0050.2450.0150.0040.0350.0000.0580.0010.2800.0000.0450.0150.1830.001
Crossing0.1480.0391.0000.0040.0080.0580.0360.0880.0620.0360.1790.1190.1180.0620.0370.4760.0070.0540.001
Distance(mi)0.0000.0030.0041.0000.3940.000-0.0100.0010.000-0.1080.0000.0050.002-0.0620.0010.004-0.0140.001-0.005
Duration_Seconds0.0020.0070.0080.3941.0000.001-0.0110.0090.001-0.1120.0010.0090.004-0.0330.0000.0080.0050.005-0.038
Give_Way0.0060.0050.0580.0000.0011.0000.0040.0090.0060.0070.0030.0080.0300.0050.0030.0720.0070.0100.000
Humidity(%)0.0150.2450.036-0.010-0.0110.0041.0000.0090.0090.0430.0060.0280.026-0.3310.0050.022-0.4640.084-0.199
Junction0.0260.0150.0880.0010.0090.0090.0091.0000.0040.0270.0090.0510.0360.0270.0050.1040.0040.0290.000
No_Exit0.0140.0040.0620.0000.0010.0060.0090.0041.0000.0070.0040.0120.0260.0090.0130.0300.0070.0060.000
Pressure(in)0.0230.0350.036-0.108-0.1120.0070.0430.0270.0071.0000.0170.0420.0030.0210.0050.0350.0780.0770.000
Railway0.0500.0000.1790.0000.0010.0030.0060.0090.0040.0171.0000.0140.0070.0100.0060.0590.0030.0070.000
Severity0.0380.0580.1190.0050.0090.0080.0280.0510.0120.0420.0141.0000.0580.0380.0050.1190.0120.0970.001
Stop0.0340.0010.1180.0020.0040.0300.0260.0360.0260.0030.0070.0581.0000.0160.0270.0480.0010.0090.000
Temperature(F)0.0100.2800.062-0.062-0.0330.005-0.3310.0270.0090.0210.0100.0380.0161.0000.0060.0470.2280.0830.088
Traffic_Calming0.0230.0000.0370.0010.0000.0030.0050.0050.0130.0050.0060.0050.0270.0061.0000.0110.0020.0060.000
Traffic_Signal0.1050.0450.4760.0040.0080.0720.0220.1040.0300.0350.0590.1190.0480.0470.0111.0000.0020.0750.002
Visibility(mi)0.0020.0150.007-0.0140.0050.007-0.4640.0040.0070.0780.0030.0120.0010.2280.0020.0021.0000.0100.055
Wind_Direction0.0230.1830.0540.0010.0050.0100.0840.0290.0060.0770.0070.0970.0090.0830.0060.0750.0101.0000.002
Wind_Speed(mph)0.0000.0010.001-0.005-0.0380.000-0.1990.0000.0000.0000.0000.0010.0000.0880.0000.0020.0550.0021.000

Missing values

2024-11-05T00:36:15.818377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-05T00:36:20.553514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SeverityDistance(mi)StreetZipcodeTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_Seconds
220.01State Route 324517636.0100.029.6710.0SW3.5OvercastFalseFalseFalseFalseFalseFalseFalseFalseTrueNight1800.0
330.01I-75 S4541735.196.029.649.0SW4.6Mostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
420.01Miamisburg Centerville Rd4545936.089.029.656.0SW3.5Mostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseTrueDay1800.0
530.01Westerville Rd4308137.997.029.637.0SSW3.5Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
620.00N Woodward Ave45417-247634.0100.029.667.0WSW3.5OvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
730.01N Main St4540534.0100.029.667.0WSW3.5OvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
820.00Notre Dame Ave45404-192333.399.029.675.0SW1.2Mostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
930.01Westerville Rd4308137.4100.029.623.0SSW4.6Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
1030.01Outerbelt S4322835.693.029.645.0WNW5.8RainFalseTrueFalseTrueFalseFalseFalseFalseFalseDay1800.0
1130.01I-70 E4306837.4100.029.623.0SSW4.6Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
SeverityDistance(mi)StreetZipcodeTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_Seconds
772838420.390I-10 E9170678.052.029.6910.0VAR6.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1723.0
772838520.000CA-60 E9255588.032.028.2010.0WNW10.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1703.0
772838620.189El Camino Real N9300373.068.029.7610.0W9.0FairFalseFalseFalseTrueFalseFalseFalseFalseFalseDay1703.0
772838720.443Santa Ana Fwy S9278075.060.029.7410.0SSW9.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1711.0
772838820.000Golden State Fwy N9133181.048.028.7810.0ESE6.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1711.0
772838920.543Pomona Fwy E9250186.040.028.9210.0W13.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1716.0
772839020.338I-8 W9210870.073.029.3910.0SW6.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1613.0
772839120.561Garden Grove Fwy9286673.064.029.7410.0SSW10.0Partly CloudyFalseFalseFalseTrueFalseFalseFalseFalseFalseDay1708.0
772839220.772San Diego Fwy S9023071.081.029.6210.0SW8.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1761.0
772839320.537CA-210 W9234679.047.028.637.0SW7.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1765.0

Duplicate rows

Most frequently occurring

SeverityDistance(mi)StreetZipcodeTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_Seconds# duplicates
6685120.229Lee Hwy22031-231846.668.030.3610.0WSW5.8ClearFalseTrueFalseFalseFalseFalseFalseFalseFalseDay3389400.028
1294320.000E Main St4732721.789.030.175.0SW6.9ClearFalseFalseFalseFalseFalseFalseFalseFalseTrueDay1751.013
1083120.000W Ridge Rd14559-102942.089.028.9810.0NE5.0CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseNight3600.012
14049346.625E Historic Columbia River Hwy9701969.063.030.0110.0VAR3.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay417709.012
12151623.189I-84 E9705825.080.029.622.0SE5.0Light SnowFalseFalseFalseFalseFalseFalseFalseFalseFalseNight28089.011
12684327.424I-84 E9705825.080.029.622.0SE5.0Light SnowFalseFalseFalseFalseFalseFalseFalseFalseFalseNight28089.011
12921830.000Augusta Rd2907375.0100.029.741.0SE13.8RainFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1661.011
13073730.000I-66 E2203061.083.030.2210.0SE9.2Mostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1737.011
13129330.000I-95 N3317978.0100.029.9110.0S5.0Partly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseDay2635.011
14048246.625E Historic Columbia River Hwy9701955.083.029.9510.0E5.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay604546.011